User activity analysis (UAA) is a promising technology for network management and network security via network traffic. Recently, deep learning (DL) has been applied into network traffic analysis for outstanding performance. These previously proposed network traffic analysis methods generally requires huge amounts of data from network users. In detail, the common methods are to collect these traffic data collected from users into cloud server for processing and analysis, which generally have great performance and are denoted as centralized methods. However, one of the biggest drawbacks of these methods is the risk of data privacy disclosure. Thus, we proposed a federated learning-based UAA method (which is named as FedeUAA) for reducing the risk of data leakage in mobile wireless networks. FedeUAA method has no requirement to upload data to cloud server, while it directly trains the DL models in local devices, and only needs to upload the knowledge (model weight or model gradient) rather than data. Simulation results demonstrated that the FedeUAA method can effectively reduce the risk of data privacy disclosure with slight performance loss.

用户活动分析（UAA）是一种通过网络流量进行网络管理和网络安全的有前途的技术。最近，深度学习 (DL) 已被应用于网络流量分析，以取得出色的性能。这些先前提出的网络流量分析方法通常需要来自网络用户的大量数据。具体来说，常见的方法是将这些从用户那里收集到的流量数据收集到云服务器中进行处理和分析，通常性能很好，称为集中式方法。然而，这些方法的最大缺点之一是数据隐私泄露的风险。因此，我们提出了一种基于联合学习的 UAA 方法（命名为 FedeUAA），用于降低移动无线网络中数据泄漏的风险。FedeUAA方法不需要上传数据到云服务器，直接在本地设备训练DL模型，只需要上传知识（模型权重或模型梯度）而不是数据。仿真结果表明，FedeUAA方法可以有效降低数据隐私泄露风险，性能损失很小。